Visual perception of smoothly curved surfaces from double-projected contour patterns

This research was designed to examine how human observers are able to perceive the 3-dimensional structure of smoothly curved surfaces from projected patterns of surface contours. Displays were generated by using a method of double projection that made it possible to cover a surface with a wide range of contour patterns of varying geometric structure and to eliminate systematic variations of image shading. The compelling impression of 3-dimensional form from these patterns provides strong evidence that the ability of observers to perceptually interpret surface contours is considerably less restrictive than would be reasonable to expect on the basis of existing computational models. Results suggest that the perceptual analysis of surface contours is able to exploit statistical regularities of contour structure over appropriately large regions of visual space.     

Representing Spatial Structure Through Maps and Language: Lord of the Rings Encodes the Spatial Structure of Middle Earth

Spatial mental representations can be derived from linguistic and non‐linguistic sources of information. This study tested whether these representations could be formed from statistical linguistic frequencies of city names, and to what extent participants differed in their performance when they estimated spatial locations from language or maps. In a computational linguistic study, we demonstrated that co‐occurrences of cities in Tolkien’s Lord of the Rings trilogy and The Hobbit predicted the authentic longitude and latitude of those cities in Middle Earth. In a human study, we showed that human spatial estimates of the location of cities were very similar regardless of whether participants read Tolkien’s texts or memorized a map of Middle Earth. However, text‐based location estimates obtained from statistical linguistic frequencies better predicted the human text‐based estimates than the human map‐based estimates. These findings suggest that language encodes spatial structure of cities, and that human cognitive map representations can come from implicit statistical linguistic patterns, from explicit non‐linguistic perceptual information, or from both.     

Grammatical pattern learning by human infants and cotton-top tamarin monkeys

There is a surprising degree of overlapping structure evident across the languages of the world. One factor leading to cross-linguistic similarities may be constraints on human learning abilities. Linguistic structures that are easier for infants to learn should predominate in human languages. If correct, then (a) human infants should more readily acquire structures that are consistent with the form of natural language, whereas (b) non-human primates' patterns of learning should be less tightly linked to the structure of human languages. Prior experiments have not directly compared laboratory-based learning of grammatical structures by human infants and non-human primates, especially under comparable testing conditions and with similar materials. Five experiments with 12-month-old human infants and adult cotton-top tamarin monkeys addressed these predictions, employing comparable methods (familiarization-discrimination) and materials. Infants rapidly acquired complex grammatical structures by using statistically predictive patterns, failing to learn structures that lacked such patterns. In contrast, the tamarins only exploited predictive patterns when learning relatively simple grammatical structures. Infant learning abilities may serve both to facilitate natural language acquisition and to impose constraints on the structure of human languages.     

Patterns, chunks, and hierarchies in serial reaction-time tasks

The impact of relational structures (i.e., the systematicity of relations between successive items) on incidental sequence learning was investigated in a serial reaction-time (SRT) task while keeping constant the statistical structure. In order to assess the influence of relational structures in stimulus and response sequences separately, the strength of relational patterns in sequences of digits as stimuli and of keystrokes as responses was orthogonally varied. In Exps. 1 and 2, the variation of relational patterns was mainly effective in the keystroke sequence. In Exp. 2, in addition to the variation of relational patterns, the presentation of stimuli was delayed at serial positions that were incongruent with the relational structure. The results show that these incongruent pauses reduced the learning of strongly structured sequences of keystrokes but improved the learning of weakly structured sequences. Experiment 3 suggests that even higher-order relations between elementary patterns are utilized to accelerate responses. The data are interpreted as evidence for the impact of relational patterns, in addition to statistical redundancies, on the formation of chunks. Reasons are discussed for the finding that relational chunking was more pronounced in the keystroke than in the digit sequences. Received: 25 June 1998 / Accepted: 3 December 1998     

Changing the tune: the structure of the input affects infants' use of absolute and relative pitch

Sequences of notes contain several different types of pitch cues, including both absolute and relative pitch information. What factors determine which of these cues are used when learning about tone sequences? Previous research suggests that infants tend to preferentially process absolute pitch patterns in continuous tone sequences, while other types of input elicit relative pitch use by infants. In order to ask whether the structure of the input influences infants’ choice of pitch cues, we presented learners with continuous tone streams in which absolute pitch cues were rendered uninformative by transposing the tone sequences. Under these circumstances, both infants and adults successfully tracked relative pitches in a statistical learning task. Implications for the role played by the structure of the input in the learning process are considered.     

Theoretical and empirical review of multinomial process tree modeling

We review a current and popular class of cognitive models calledmultinomial processing tree (MPT) models. MPT models are simple, substantively motivated statistical models that can be applied to categorical data. They are useful as data-analysis tools for measuring underlying or latent cognitive capacities and as simple models for representing and testing competing psychological theories. We formally describe the cognitive structure and parametric properties of the class of MPT models and provide an inferential statistical analysis for the entire class. Following this, we provide a comprehensive review of over 80 applications of MPT models to a variety of substantive areas in cognitive psychology, including various types of human memory, visual and auditory perception, and logical reasoning. We then address a number of theoretical issues relevant to the creation and evaluation of MPT models, including model development, model validity, discrete-state assumptions, statistical issues, and the relation between MPT models and other mathematical models. In the conclusion, we consider the current role of MPT models in psychological research and possible future directions.     

Absolute pitch in infancy and adulthood: the role of tonal structure

Despite many similarities in infant and adult auditory processing, the literature suggests that two aspects of music perception, pitch processing and knowledge of tonal structure, change over development. The current experiments assess the use of absolute and relative pitch cues in a tone sequence statistical learning task containing tonal structure. The results suggest that infants preferentially process absolute pitch patterns in continuous tone sequences, supporting the hypothesis that absolute pitch is present in infancy, whereas adults tracked both absolute and relative pitch patterns. Infants and adults detected the tonal structure in the input, suggesting that humans are attuned to basic aspects of tonality early in life.     

Numerosity discrimination in deep neural networks: Initial competence,developmental refinement and experience statistics

Both humans and non‐human animals exhibit sensitivity to the approximate number of items in a visual array, as indexed by their performance in numerosity discrimination tasks, and even neonates can detect changes in numerosity. These findings are often interpreted as evidence for an innate ‘number sense’. However, recent simulation work has challenged this view by showing that human‐like sensitivity to numerosity can emerge in deep neural networks that build an internal model of the sensory data. This emergentist perspective posits a central role for experience in shaping our number sense and might explain why numerical acuity progressively increases over the course of development. Here we substantiate this hypothesis by introducing a progressive unsupervised deep learning algorithm, which allows us to model the development of numerical acuity through experience. We also investigate how the statistical distribution of numerical and non‐numerical features in natural environments affects the emergence of numerosity representations in the computational model. Our simulations show that deep networks can exhibit numerosity sensitivity prior to any training, as well as a progressive developmental refinement that is modulated by the statistical structure of the learning environment. To validate our simulations, we offer a refinement to the quantitative characterization of the developmental patterns observed in human children. Overall, our findings suggest that it may not be necessary to assume that animals are endowed with a dedicated system for processing numerosity, since domain‐general learning mechanisms can capture key characteristics others have attributed to an evolutionarily specialized number system.     

The relative influence of advice from human experts and statistical methods on forecast adjustments

Decision makers and forecasters often receive advice from different sources including human experts and statistical methods. This research examines, in the context of stock price forecasting, how the apparent source of the advice affects the attention that is paid to it when the mode of delivery of the advice is identical for both sources. In Study 1, two groups of participants were given the same advised point and interval forecasts. One group was told that these were the advice of a human expert and the other that they were generated by a statistical forecasting method. The participants were then asked to adjust forecasts they had previously made in light of this advice. While in both cases the advice led to improved point forecast accuracy and better calibration of the prediction intervals, the advice which apparently emanated from a statistical method was discounted much more severely. In Study 2, participants were provided with advice from two sources. When the participants were told that both sources were either human experts or both were statistical methods, the apparent statistical‐based advice had the same influence on the adjusted estimates as the advice that appeared to come from a human expert. However when the apparent sources of advice were different, much greater attention was paid to the advice that apparently came from a human expert. Theories of advice utilization are used to identify why the advice of a human expert is likely to be preferred to advice from a statistical method. Copyright © 2009 John Wiley & Sons, Ltd.     

An examination of graduate students' statistical judgments: statistical and fuzzy set approaches

The present study examined how statistical significance levels are treated and interpreted by graduate students who use hypothesis-testing in their scientific investigation. To test underlying psychological aspects of hypothesis-testing, the idea of fuzzy set theory was employed to identify the uncertain points in judgments. 34 graduate students in a psychology department made judgments about hypothetical statistical decisions. The results indicated that (1) the majority of these students treated significance levels on a continuum and rated them according to the magnitude of statistical significance; (2) the subjects shifted their decisions based on the types of hypothetical scenarios but not by the sample sizes; instead, they interpreted a smaller sample size as being less reliable. (3) The subjects frequently chose formally used statistical terms, e.g., Significant and Not Significant, more than graduated verbal expressions, e.g., Marginally Significant and Borderline Significant; and (4) the Fuzziness (degree of confidence in decision-making) was dependent on individuals and existed more in the critical points of transition where judgments are most difficult. The Fuzziness Index illustrated the subtle shifts of human decision-making patterns in statistical judgments. Underlying decision uncertainties and difficulties can be illustrated by functions generated from fuzzy set theory, which may more closely resemble human psychological mechanism. This integrative study of fuzzy set theory and behavioral measurements appears to provide a technique that is more natural for examining and understanding imprecise boundaries of human decisions.     

Subtask analysis of process data through a predictive model

Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem-solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach.     

A sustainable future for humanity? How can psychology help?

The sustainability of human life on Earth in the future is in danger. Human actions are producing many harmful and possibly irreversible changes to the environmental conditions that support life on Earth. This article summarizes major threats to Earth's environment, including global warming, ozone layer destruction, exhaustion of fisheries and agricultural land, and widespread exposure to toxic chemicals. Unless they are overcome, these changes will make human life increasingly miserable and eventually may make Earth nearly uninhabitable for future generations. These threats are caused by patterns of human behavior, particularly over-population and over-consumption. Urgent changes to human lifestyles and cultural practices are required for the world to escape ecological disaster, and psychologists should lead the way in helping people adopt sustainable patterns of living. Specific steps toward that goal are proposed in this and the following four articles.     

Quantitative Standards for Absolute Linguistic Universals

Absolute linguistic universals are often justified by cross‐linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross‐linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods—frequentist and Bayesian—and show that in both it is possible to find strict linguistic universals, but that the numbers of independent languages necessary to do so is generally unachievable. This suggests that methods other than typological statistics are necessary to establish absolute properties of human language, and thus that many of the purported universals in linguistics have not received sufficient empirical justification.     

Statistical learning within and between modalities: pitting abstract against stimulus-specific representations

When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.     

Inferring action structure and causal relationships in continuous sequences of human action

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.     

The search for dimensional structure differences between normality and abnormality: a statistical review of published data on personality and psychopathology

A statistical review of published data for 37 personality and psychopathology inventories was conducted to determine whether there are dimensional structure differences between clinical and nonclinical respondents. Correlation and factor-loading matrices from multiscale inventories and from specialized measures were tested for structural invariance across populations. There was relatively consistent evidence for high levels of similarity between normal and abnormal populations both in the number of factors that exist in the data matrices and in the factor patterns. The dimensional universes of normality and abnormality are apparently the same, at least according to data derived from contemporary assessment instruments. Categorical-taxonic differences between clinical and nonclinical populations, which were not examined, may nevertheless exist within contexts of dimensional structure similarity.     

Visual statistical learning at basic and subordinate category levels in real-world images

Visual statistical learning (VSL) has been proposed as a powerful mechanism underlying the striking ability of human observers to handle complex visual environments. Previous studies have shown that VSL can occur when statistical information is embedded at multiple levels of abstraction, such as at semantically different category levels. In the present study, we further examined whether statistical regularities at a basic category level (e.g., a regular sequence of a bird, then a car, and then a dog) could influence the ability to extract statistical regularities at the subordinate level (e.g., a regular sequence of a parrot, then a sports car, and then an Eskimo dog). In the familiarization phase, participants were exposed to a stream of real-world images whose semantic categories had temporal regularities. Importantly, the temporal regularities existed at both the basic and subordinate levels, or the regularities existed at only the subordinate level, depending on the experimental condition. After completing the familiarization, participants performed a surprise two-alternative forced choice (2AFC) task for a familiarity judgment between two triplets in which the temporal regularities were either preserved or not preserved. Our results showed that the existence of statistical regularities at the basic level did not influence VSL at the subordinate level. The subsequent experiments showed these results consistently even when the basic-level categories had to be explicitly recognized and when the stimuli were not easily categorized at their subordinate level. Our results suggest that VSL is constrained to learn a particular level of patterns when patterns are presented across multiple levels.     

Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural‐language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach.     

A chimpanzee (Pan troglodytes) analogue of cross-national generalization of personality structure: zoological parks and an African sanctuary

Six personality factors, including five resembling the human Big Five, had previously been identified in a separate group of zoo-housed chimpanzees. Comparability of chimpanzee personality factor structure was examined in two highly contrasting habitats: zoos and a large African sanctuary. Questionnaires for the zoo chimpanzees were in English, while most for the chimpanzees in the sanctuary were in French. Differences between the two settings were sufficiently extensive to make them analogous to cross-national human personality studies. Internal consistencies for five of the six factors did not differ between the two samples. The patterns of correlations between the unit-weighted factors were also similar for the two samples. Data from these two samples were pooled and factor analyzed. The resulting factor structure was then rotated to the factor structure described in the original study of chimpanzee personality. Dominance, Extraversion, Dependability, and Agreeableness had high congruences. Emotionality and Openness did not, but the items that had the highest loadings were consistent with the factors' definitions. Finally, sex and age effects for all factors generalized across habitats.     

A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data

Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance tests that are familiar to psychologists, such as t‐tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within‐subject and between‐group differences, conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed‐effects model that are designed to handle autocorrelated data, such as within‐subject repeated‐measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, we compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based on an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.